The present invention relates, in general, to automated image recognition, and more particularly, to a neural network-based image recognition system for cancerous tissue cell detection.
Bladder cancer is among one of the top causes of cancer-related deaths in the United States. In 1993, approximately 52,000 cases of bladder cancer were reported in the United States and 10,000 deaths were attributed to this disease. Early detection is essential in minimizing the risks involved with bladder cancer. The detection of bladder cancer is traditionally performed through cystoscopy, through the quantitation of plasma components of urine, or through detailed analysis of stained bladder cells obtained from urine or from a bladder wash.
Cystoscopy is the visual inspection of the bladder using a fiber optic device. Normally performed by a urologist, cystoscopy is discomforting to the patient and exposes the patient to the risks and costs associated with surgery. Further, cystoscopy only detects cancerous cells after the tumor has progressed to an advanced stage.
Significant progress in the detection and isolation of bladder tumor specific antigens has linked bladder cancer with an elevation of normal protein components in the plasma or urine of cancer patients. Thus, bladder cancer may be detected by identifying the abnormal presence of materials in the bladder cells. Since these tests are non-invasive, they could be routinely utilized to test those in high risk groups for early symptoms of bladder cancer. However, an approach using serum and plasma related components in urine appears to have limited usefulness in the early detection of bladder cancer as many of these identical components are also present in increased concentrations in urine from patients with non-neoplastic disease.
Another promising approach involves the analysis of bladder cells obtained from urine or a bladder wash. In this process, bladder cells are extracted from urine or a bladder wash. They are then prepared using conventional staining techniques such as the Papanicolaou technique for highlighting the region of interests in the sample cells. Conventionally, these cells are visually inspected for signs of cancer. Typically, after a cyto-technician has screened the sample cells, the final diagnostic is performed by an expert cytopathologist. This process is labor intensive because it requires exhausting inspection of thousands of cells. Naturally, the tedium and fatigue imposed upon the technician and the cytopathologist result in a high false negative rate.
Due to the vast amount of data to be processed, automation of the bladder cancer cell detection process is quite desirable. Various techniques have been proposed for the automated detection of cancer. Predominately, these prior attempts have relied on techniques such as feature extraction, template matching and other statistical or algorithmic methods. For instance, Melder and Koss described a decision tree representing the hierarchical classification scheme to classify extracted features from the triage of objects encountered in the urinary sediment. Karl K. Melder and Leopold G. Koss, xe2x80x9cAutomated Image Analysis in the Diagnosis of Bladder Cancer,xe2x80x9d 26 Applied Optics 16, 3367 (1987). Melder and Koss discussed the use of step-wise linear discriminant analysis in which features were automatically selected for the discriminant functions based on the pooled covariance matrix of more than sixty (60) cell features. Christen, et al., discussed the application of a linear discriminant model from the SPSS/PC+ statistical package to the classification of cancerous cells. Christen, et al., xe2x80x9cChromatin Texture Features in Hematoxylin and Eosin-Stained Prostate Tissue,xe2x80x9d 16 Analytical and Quantitative Cytology and Histology, 16, 383 (1993).
Recently, artificial neural networks have been applied to the cancer detection process. This step is a logical extension of the pattern recognition capability of artificial neural networks. Kunihiko Fukushima, xe2x80x9cNeural Network Model for Selective Attention in Visual Pattern Recognition and Associative Recall,xe2x80x9d 26 Applied Optics 23, 4985 (1987); Dwight D. Egbert, et al., xe2x80x9cPreprocessing of Biomedical Images for Neurocomputer Analysis,xe2x80x9d IEEE Int""l Conference on Neural Networks I-561 (Jul. 24-27, 1988).
A variety of neural network topologies have been experimented with. By way of illustration, some of these neural network models include the Perceptron, described in U.S. Pat. No. 3,287,649 issued to F. Rosenblatt and further described in M. Minsky and S. Papert, xe2x80x9cPerceptrons, An Introduction to Computational Geometry,xe2x80x9d (MIT Press 1988); the Hopfield Net, described in U.S. Pat. Nos. 4,660,166 and 4,719,591 issued to J. Hopfield; xe2x80x9cThe Hamming Network and Kohonen Self-Organizing Maps,xe2x80x9d described in R. Lippman, xe2x80x9cAn Introduction to Computing with Neural Nets,xe2x80x9d IEEE ASSP Magazine, April 1987 at 4-22; D. E. Rumelhart, G. E. Hinton and R. J. Williams, xe2x80x9cLearning Internal Representations by Error Propagation,xe2x80x9d in 1 Parallel Distributed Processing 318-362 (D. E. Rumelhart, et al. eds., 1986); and G. O. Stone, xe2x80x9cAn Analysis of the Delta Rule and the Learning of Statistical Associations,xe2x80x9d in 1 Parallel Distributed Processing 444-459 (D. E. Rumelhart, et al. eds., 1986).
A particularly robust type of neural network is referred to as the back-propagation network. The training process for back-propagation type neural networks starts by modifying the weights at the output layer. Once the weights in the output layer have been altered, they can act as targets for the outputs of the hidden layer, changing the weights in the hidden layer following the same procedure as above. This way the corrections are back-propagated to eventually reach the input layer. After reaching the input layer, a new test is entered and forward propagation takes place again. This process is repeated until either a preselected allowable error is achieved or a maximum number of training- cycles has been executed.
Due to the sheer number of computational cycles in the training process, the computation of the activation function is crucial to the performance of the neural network. A traditional back-propagation neural network utilizes the non-linear sigmoid function as the activation function in its neurons. The effectiveness of traditional back-propagation neural networks is limited by the fact that the training procedure does not guarantee convergence to the global minima.
Traditional back-propagation neural networks have been applied to bladder cells in Ciamac Moallemi, xe2x80x9cClassifying Cells for Cancer Diagnosis Using Neural Network,xe2x80x9d 6 IEEE Expert 6, 8 (1991). Moallemi describes the application of a conventional neural network in the classification of noisy particles versus cell images, including cancerous and non-cancerous bladder cells. However, Moallemi does not teach the detection of malignant cells using a neural network.
The application of neural networks to the classification of cytological specimens is discussed in U.S. Pat. No. 4,965,725 to Rutenberg. Rutenberg describes the use of a two-staged classifier system. The first classifier is a statistical classifier which identifies cell nuclei of interest by measurement of their integrated optical density, or nuclear stain density, defined as the sum of the pixel gray values for the object. Rutenberg discloses that, compared to normal cells, malignant cells tend to possess a larger, more densely staining nucleus. Based on the data provided by the primary classifier, Rutenberg further employs a neural network as a secondary classifier for evaluating the nucleus and its surrounding cytoplasm based on the observation that the ratio between the nucleus and the cytoplasm is an important indicator for malignant cell classification. However, Rutenberg does not utilize other predictive information such as the pgDNA value of a cell.
One limitation with conventional back-propagation network is that it imposes considerable computational demands under its iterative gradient descent method. With the number of training cycles often numbering into the tens and hundreds of thousands for moderately complex problems, the usefulness of a neural network trained according to conventional methodology is limited. For instance, the training of Moallemi""s neural network required a few hours on the Convex C-120, a mini-supercomputer. Additionally, conventional back-propagation neural networks have difficulty adjusting their learning rate, defined as the step-size taken along the path of steepest descent (i.e., the gradient vector) or other path of convergence to arrive at a local minimum.
Despite the above-described applications of neural networks to analyze biological samples, a need exists for a neural network that can be trained relatively rapidly using commonly available computers. There exists a current need for an improved gradient descent learning method that can more quickly find a global or local minimum for a given gradient vector and additionally adjust the gradient vector after identifying a ravine. Other inefficiencies are also present in conventional training methods which relate to the speed at which the set of weighting factors converge at the desired result, as will be described in further detail below.
In accordance with one aspect of the invention, a system is disclosed for self-adaptively and robustly distinguishing normal from abnormal tissue cells.
In accordance with a further aspect of the invention, a urological cancer detection system using a neural network is disclosed.
In accordance with yet another aspect of the invention, a system is disclosed for applying a neural network having a combination of activation functions, among them gaussian, sigmoid and sinusoid functions.
In accordance with yet another aspect of the invention, a cancer detection system using a rapidly trained neural network is disclosed.
In accordance with yet another aspect of the invention, a neural network is provided which detects cancerous cells by analyzing raw images of the cell and providing the imaging information derived from the pixels of the images to a neural network.
In accordance with yet another aspect of the invention, a neural network is provided which performs recognition of cancerous cells using information derived from an image of the cells, among others, the area, the average intensity, the shape, the texture, and the DNA content (pgDNA) of the cells.
In accordance with yet another aspect of the invention, a neural network is provided which performs such recognition of cancerous cells using textural information derived from an image of the cells, among them angular second moment, contrast, coefficient of correlation, sum of squares, difference moment, inverse difference moment, sum average, sum variance, sum entropy, entry, difference variance, difference entropy, information measures, maximal correlation coefficient, coefficient of variation, peak transition probability, diagonal variance, diagonal moment, second diagonal moment, product moment, triangular symmetry 11 and blobness.